Homomorphic Encryption for Fuzzy Data Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing technologies face challenges in efficiently handling encrypted data for similarity queries across networks, particularly due to high computational overheads and latency issues, which hinder effective data management and privacy protection in large-scale data environments.
Innovation Solution
A computer-implemented method using homomorphic encryption and probabilistic data structures, such as Bloom filters, to perform group-wise data similarity queries, reducing computational complexity and latency by aggregating data into matrices for efficient processing and matching operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If homomorphic encryption is used to process encrypted data for similarity queries, then data privacy is protected, but computational overhead increases and turnaround time becomes prohibitively slow
Solution Approach 1:
The patent segments the encrypted data processing into distinct phases: encoding data into probabilistic data structures (Bloom filters), encrypting these structures using homomorphic encryption, and then performing similarity computations on the encrypted representations. This segmentation allows each phase to be optimized independently, balancing privacy protection with computational efficiency.
Solution Approach 2:
The patent changes the parameter representation by transforming raw data into encoded forms (Bloom filter encodings) before encryption. This parameter transformation reduces the computational complexity of subsequent similarity operations while maintaining the ability to perform accurate matching on the encrypted data.
2Measurement precision
If traditional pair-wise matching is used for data similarity queries, then matching accuracy is maintained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent merges multiple individual data item comparisons into batch operations by organizing data into structured representations (e.g., matrices of Bloom filter encodings). This allows simultaneous processing of multiple data pairs, dramatically reducing the total number of operations required while maintaining matching accuracy through the properties of the underlying probabilistic data structures.
3Reliability
If data is encrypted to protect privacy, then data security is improved, but data processing and matching operations cannot be performed
Solution Approach 1:
The patent introduces probabilistic data structures (Bloom filters) as an intermediary representation between the original data and the encrypted form. These structures serve as a mediator that preserves the essential matching properties of the data while enabling efficient homomorphic operations, thus bridging the gap between security requirements and processing capabilities.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Computer-implemented methods and systems (DMS) for facilitating data similarity queries across a network (CN) of data memories (DM1, DM2). The disclosed methods and system are configured for matching data items held in the data memories (DM1, DM2) in a probabilistic manner with cryptographic protection of the data items. The data matching methods and systems (DMS) are robust against inconsistencies within the data to be matched (such as typographical errors, minor mismatches etc), within a certain predetermined similarity threshold (q), usually described as a percentage. The disclosed methods and systems (DMS) allow fast, low latency turnaround by aggregating the data items to be matched into data structures (M1, M2) that facilitate group-wise matching as opposed to pair-wise matching.